This study aims to evaluate theories and ideas about social values and determine the high quality of virtues that potentially change social practices, thinking, self-awareness, and behavior of the individual and society. The relevance of the study of value components is determined by the fact that such values as “spirituality and morality”, “responsibility”, “justice”, “rationality”, and “security” are capable of capturing the greatest value of many interests, which allows for the integration of society. An experimental study was conducted using sociological research methods based on developed questionnaires with questions touching on the parameters of sustainable development of society, determining the high quality of virtues and behavior of the individual and society. The study was conducted from May to June 2023 (N = 1387). Based on Demoethical values, special attention is paid to global problems related to climate change and inefficient use of energy and water resources, thereby achieving the Sustainable Development Goals. As a result of the study, Demoethical values are revealed in interaction with the economic components of demography, democracy, and demoeconomics as a tool for social transformation, as they shape the harmonious vision of the world, human behavior, decisions, and relationships with other people.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

This paper presents a numerical method for solving a nonlinear age-structured population model based on a set of piecewise constant orthogonal functions. The block-pulse functions (BPFs) method is applied to determine the numerical solution of a non-classic type of partial differential equation with an integral boundary condition. BPFs duo to the simple structure can efficiently approximate the solution of systems with local or non-local boundary conditions. Numerical results reveal the accuracy of the proposed method even for the long term simulations.